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Predicting large fetuses at birth: do multiple ultrasound examinations and longitudinal statistical modelling improve prediction?

Authors

  • Jun Zhang,

    1. MOE and Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine and School of Public Health, Shanghai, China
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  • Sungduk Kim,

    1. Division of Epidemiology, Statistics and Prevention Research, Eunice Shriver Kennedy National Institute of Child Health and Human Development, NIH, Bethesda, MD, USA
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  • Jagteshwar Grewal,

    1. Division of Epidemiology, Statistics and Prevention Research, Eunice Shriver Kennedy National Institute of Child Health and Human Development, NIH, Bethesda, MD, USA
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  • Paul S. Albert

    Corresponding author
    1. Division of Epidemiology, Statistics and Prevention Research, Eunice Shriver Kennedy National Institute of Child Health and Human Development, NIH, Bethesda, MD, USA
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Dr Paul Albert, Biostatistics & Bioinformatics Branch, Division of Epidemiology, Statistics and Prevention Research, Eunice Shriver Kennedy National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA. E-mail: albertp@mail.nih.gov

Summary

Zhang J, Kim S, Grewal J, Albert PS. Predicting large fetuses at birth: do multiple ultrasound examinations and longitudinal statistical modelling improve prediction? Paediatric and Perinatal Epidemiology 2012; 26: 199–207.

Predicting large fetuses at birth has long been a challenge in obstetric practice. We examined whether ultrasound examinations at multiple times during pregnancy improve the accuracy of prediction using repeated, longitudinal statistical modelling, and whether adding maternal characteristics improves the accuracy of prediction. We used data from a previous study conducted in Norway and Sweden from 1986 to 1989 in which each pregnant woman had four ultrasound examinations at around 17, 25, 33 and 37 weeks of gestation. At birth, infant size was classified as large-for-gestational age (LGA, >90th centile) and macrosomia (>4000 g) or not. We used a longitudinal random effects model with quadratic fixed and random effects to predict term LGA and macrosomia at birth. Receiver–operator curves and mean-squared error were used to measure accuracy of the prediction. Ultrasound examination around 37 weeks had the best accuracy in predicting LGA and macrosomia at birth. Adding multiple ultrasound examinations at earlier gestations did not improve the accuracy. Adjusting for maternal characteristics had limited impact on the accuracy of prediction. Thus, a single ultrasound examination at late gestation close to birth is the simplest method currently available to predict LGA and macrosomia.

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